School for Business

Social and Decision Sciences

Jacobs University Bremen

Abstract

Over the past two years, several waves of COVID-19 infections caused by different mu-

tations of the SARS-CoV-2 virus have swept across the globe, claiming lives, stressing

healthcare systems, and changing many different facets of our day-to-day experience.

the pandemic, data on the number of infections and COVID-related deaths has been

published daily and made available to the public. Media, politicians and individuals

use this data to build their narratives about the pandemic, discuss its evolution, justify

the measures taken and discuss different prevention strategies against the spread of

the virus. These goals and their priorities were adapted as dynamically as the virus

mutated and the pandemic changed pace, but as with any other data visualisation, the

general principles remain the same; ensuring clear understanding by organising graphics

in such a way that the story of the data is told most effectively.

In recent decades, many media organisations have established data teams that received

publishing, with COVID-19 delivering many excellent applications, often presented in

an interactive visual format on the web, such as dashboards (Koch 2021).

At the same time, we still see a lot of defective graphics disseminated and shared;

some that violate fundamental statistical and visualisation design principles such as

accuracy, relevance, timeliness, clarity, coherence, and reproducibility. These principles

have been laid out in numerous standards for statistical reporting in the application

areas, such as the ESS standard for quality reports (Commission 2020), the CONSORT,

PRISMA, CHEERs guidelines, and others (UK EQUATOR Centre 2022). The use of

numbers, their graphic representation and the systematic creation of charts and tables

from data in the mass media have been accompanied from the beginning by efforts

has vastly improved over the past 100 years, we still face many of the same problems

when designing charts which communicate data accurately and effectively.

In other areas, such as visualisation of uncertainty, the pandemic presents new chal-

lenges for scientific communication. How do we effectively show the uncertainty in the

data amid flawed tests, new strains, changing diagnostic criteria, and incredible vari-

ability in global public health infrastructure? Moreover, how do we do this without

further decreasing public trust in institutions responsible for shepharding us through

the pandemic? While most designers chose to side-step these questions in the moment,

as we assess the pandemic in graphical form, we should also consider what was omitted

readers while focusing on key takeaway points. This approach was developed to in-

crease effective communication between stakeholders such as healthcare providers and

patients (Harding-Zentrum fur Risikokompetenz 2022). Such "fact boxes" combined

with the dynamic layer of the internet allowed for illustrative simulations of the spread

of the epidemic, for example as presented on 14 March 2020 in the Washington Post

with the title "Why outbreaks like coronavirus spread exponentially, and how to flatten

the curve" (Stevens 2020). Nevertheless, both the complexity of phenomena and the

"bipolarity" of statistical thinking remain a challenge. While human thinking tends

to simplify patterns, and political communication also prefers a simple cause-effect re-

lationship, real phenomena are often multivariate. Thus, in studying COVID-19 and

attribution problematic. However, statisticians who point out these limitations are at

risk of having their statements pulled out to support one side in a polarised debate

(McConway and Spiegelhalter 2021).

Finally, it is critical that data communication take into account the intended audience.

Graphics created to communicate with public health officials should be different from

those created for the purpose of informing the public. Designers must carefully consider

the visual capabilities, attention constraints, and mathematical sophistication of the

audience when creating a chart, but in this era of instantaneous global communication,

charts designed for specific audiences may still reach a large population of viewers

with different capabilities. In this paper, we focus primarily on graphics which are

information. While the overall goal of a data visualisation is to tell the story of the

data clearly and effectively, how this story is conveyed involves a series of design choices:

Which variables are shown, What limits are imposed, What visual forms are used to

language, education level, likely medium used to access the visualisation (computer,

physical newspaper, tablet, cell phone) – all of these considerations also impact the

design of the chart and what ideas the designer considers during the creative process.

Once the basic form of the chart is determined, there are additional considerations

that may be specified by the stylistic conventions of different news organisations, but

may also be deliberately chosen for visual effect: colour palettes, fonts, spatial layouts,

and annotations may be used to highlight information, convey additional points of a

narrative, or provide helpful information about how to read an unfamiliar chart. While

each of these design choices could be (and have been) the subject of additional paper(s),

it is important to acknowledge that the graphics we analyse in this paper did not simply

pop into being – they are the product of a series of decisions, some consciously made and

others imposed by convention or technological capabilities. We will highlight particular

cases we can provide contemporary discussions of the design process and reactions to

the charts we highlight in this paper. Where such examples are not provided, that

should in no way be taken as evidence that the design and testing process was less

formal; it may only have been less public.

Our focus in this paper is to highlight, analyse, and discuss a selection of charts and

graphics which caught our attention during the initial, real-time experience of the pan-

demic. As we primarily consume media from western Europe and the United States,

our assessment is primarily limited to news outlets catering to these audiences; a ret-

rospective global analysis of COVID graphics would certainly be interesting, but is

cases and deaths during this initial period were commonly visualised by choropleth

maps, see for example Figure 1 showing the total number of infections reported in each

country as of January 14, 2022. A central element of the narrative during March 2020

was the ubiquity of the disease and the accompanying global impact.

and Son 2008) that may be of particular concern when visualising epidemic disease

spread. In addition, the visual impression of any map-based illustration depends on

the projection chosen for the underlying map; the examples in Figure 1 and Figure 2

colour). Various algorithms can be used to modify geographic boundaries to maintain

spatial continuity, or shapes such as hexagons or squares can be used to represent geo-

graphic entities (as in Figure 3a); with either choice, geometry, topology, or both must

be sacrificed. While these methods are more popular in the academic visualisation

literature, including analysis of the spread of COVID-19 (Yalcin 2022), they were not

overly common in the media for global representations of COVID information. This

may be because cartograms require more visual effort on the part of the viewer; in

addition, news organisations may be reluctant to assume their readers have the geo-

Early in the pandemic, as case numbers grew exponentially without appropriate public

health measures to restrain them, the scale problem for choropleth maps was particu-

Figure 12: A screenshot from Stats on Clock’s bar chart race showing counts of COVID

cases over time throughout the pandemic (Stats On Clock 2022).

focusing viewer attention on changes in relative totals of case counts, the graphic tends

to hide steady, proportional increases in cases across the globe, making it somewhat

difficult to perceive large global trends relative to small changes in comparative case

counts.

As people grappled with the scale of the pandemic and the lives lost as a result, a

different set of data displays attempted to provide context to this loss over time. In

the New York times, this took the form of a story showing the accumulation of the first

oft attributed to Stalin: "One death is a tragedy; a million deaths is a statistic" (Quote

Investigator 2016).

A slightly different approach by the BBC’s Visual and Data Journalism team, shown

in Figure 13, displayed the COVID cases and deaths as a growing flower, with the stem

proportional to the number of cases and the flower petals showing the deaths over time

(The BBC Visual and Data Journalism team 2020). The visualisation comes with sound

as well, an attempt to make the pandemic an auditory experience as well as a visual

one. The form, animation, and auditory experience are all indicative of an emotional

appeal (Kostelnick 2016; D’Ignazio and Klein 2020); with the flower representation as

an explicitly recognized symbol of grief; it is clearly not about showing specific case

disease at several different points during the pandemic. Unsurprisingly, the prevailing

visual narrative focused on comparisons often fueled by political rivalry, historical de-

pendencies, recent withdrawal from supranational institutions or regional competitions.

An ongoing debate about the true extent of the dangers of COVID-19 and how best

to combat it, combined with daily availability and public access to data across admin-

istrative levels, fostered ongoing competition and the use of leaderboards to show how

bad case counts were somewhere else.

and provide a good way to show the “leading” countries or regions. Emphasis on spe-

cific countries is either preconfigured by the creator of the chart or can be modified

interactively in the online version by check boxes and drop-down menus. These bar

charts also allow easy adjustment of the count data to population size or other mean-

ingful standard. Streamgraphs (see lower image in Figure 14) are a special type of

stacked area graphs and became quite popular around 2008 when they were used by

the New York Times to visualise box office results of movies (Di Bartolomeo and Hu

2016). Their usefulness depends heavily on the clarity of the pattern, as well as design

choices such as the ordering of the different groups. Small variations in the proportions

of the groups are almost impossible to detect, but streamgraphs give a good indication

with different populations or infection rates in the same chart. As noted previously,

however, interpreting log scales requires levels of numerical sophistication that may

not be appropriate for the general public. Even researchers do not always read and